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A ultra-lightweight 3D renderer of the Tensorflow/Keras neural network architectures. This Library is working on Matplotlib visualization for now. In future the visualization can be moved to plotly for a more interactive visual of the neural network architecture.

**Note:** *For now the rendering is working in Jupyter only Google Colab support is in works.*

For more details visit NetPlot

How to use it

Install with Pip

`pip install netplot`

Notebook Codelets

```
from netplot import ModelPlot
import tensorflow as tf
import numpy as np
```

`%matplotlib notebook`

```
X_input = tf.keras.layers.Input(shape=(32,32,3))
X = tf.keras.layers.Conv2D(4, 3, activation='relu')(X_input)
X = tf.keras.layers.MaxPool2D(2,2)(X)
X = tf.keras.layers.Conv2D(16, 3, activation='relu')(X)
X = tf.keras.layers.MaxPool2D(2,2)(X)
X = tf.keras.layers.Conv2D(8, 3, activation='relu')(X)
X = tf.keras.layers.MaxPool2D(2,2)(X)
X = tf.keras.layers.Flatten()(X)
X = tf.keras.layers.Dense(10, activation='relu')(X)
X = tf.keras.layers.Dense(2, activation='softmax')(X)
model = tf.keras.models.Model(inputs=X_input, outputs=X)
```

```
modelplot = ModelPlot(model=model, grid=True, connection=True, linewidth=0.1)
modelplot.show()
```